{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "from base_dataset import load_core_set_data\n",
    "from simulation.simulator import FDTDSimulator\n",
    "from simulation.student import LSTMPredictor, DataTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据\n",
    "\n",
    "## 半径矩阵\n",
    "\n",
    "```python\n",
    "radius_save_path = \"data/radius_matrix.pth\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[4.2537, 7.2491, 6.1717, 9.2729, 4.9853, 9.8473, 3.1820, 9.3614, 8.1104,\n",
      "         9.0288],\n",
      "        [2.3149, 8.7649, 5.0795, 8.0234, 4.8227, 8.7122, 0.7336, 2.7354, 7.6845,\n",
      "         6.5359],\n",
      "        [6.5778, 9.8060, 0.8749, 7.8437, 6.0056, 7.5152, 8.9349, 6.0827, 4.3046,\n",
      "         7.7731],\n",
      "        [0.3096, 1.4307, 0.4254, 2.0687, 3.2475, 0.8592, 9.9883, 3.1064, 7.1883,\n",
      "         2.8260],\n",
      "        [6.0670, 4.3900, 8.1416, 5.1876, 1.6904, 2.8222, 9.9509, 8.0686, 6.5593,\n",
      "         5.9664],\n",
      "        [9.7981, 5.3376, 9.5862, 0.3684, 5.5181, 7.1683, 3.2136, 5.2784, 1.8340,\n",
      "         9.4031],\n",
      "        [2.1909, 0.4959, 9.4064, 1.8535, 2.7756, 7.6035, 9.4730, 6.0837, 7.2479,\n",
      "         1.7767],\n",
      "        [2.4067, 8.6760, 5.0395, 6.0531, 6.9528, 0.0580, 0.6878, 2.1934, 4.4764,\n",
      "         4.8344],\n",
      "        [4.9628, 6.8850, 0.1238, 5.7276, 2.3660, 0.6239, 9.2926, 6.6920, 3.6652,\n",
      "         1.5679],\n",
      "        [2.8365, 5.7207, 1.2948, 2.9172, 4.4617, 8.5791, 4.1536, 8.3898, 6.0025,\n",
      "         7.9040]])\n"
     ]
    }
   ],
   "source": [
    "radius_save_path = \"data/radius_matrix.pth\"\n",
    "\n",
    "if os.path.exists(radius_save_path):\n",
    "    radius_matrix = torch.load(radius_save_path, weights_only=False)\n",
    "else:\n",
    "    radius_matrix = torch.rand(10, 10) * 10\n",
    "    torch.save(radius_matrix, radius_save_path)\n",
    "\n",
    "print(radius_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1200, 10)\n",
      "(200, 10)\n"
     ]
    }
   ],
   "source": [
    "class FDTDDataset(Dataset):\n",
    "    def __init__(self, data, labels):\n",
    "        self.data = torch.FloatTensor(data)\n",
    "        self.labels = torch.FloatTensor(labels)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]\n",
    "\n",
    "\n",
    "train_data, _, test_data, _ = load_core_set_data()\n",
    "\n",
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成知识蒸馏数据\n",
    "\n",
    "调用simulator生成蒸馏用标签、调用data_transformer生成蒸馏用输入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device:  cuda\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "print(\"device: \", device)\n",
    "\n",
    "assert device == \"cuda\", \"CUDA is not available\"\n",
    "\n",
    "simulator = FDTDSimulator(radius_matrix=radius_matrix)\n",
    "\n",
    "data_transformer = DataTransformer(device=device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1200/1200 [2:49:13<00:00,  8.46s/it] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1200, 1000, 11])\n",
      "torch.Size([1200, 1000, 10])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_labels = torch.tensor([], device=device)\n",
    "train_inputs = torch.tensor([], device=device)\n",
    "for data in tqdm(train_data):\n",
    "    inputs = torch.tensor(data)\n",
    "    outputs = simulator(inputs).detach()\n",
    "    train_labels = torch.cat((train_labels, outputs), dim=0)\n",
    "\n",
    "    inputs = data_transformer(inputs).unsqueeze(0)\n",
    "    train_inputs = torch.cat((train_inputs, inputs), dim=0)\n",
    "\n",
    "print(train_inputs.shape)\n",
    "print(train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs = train_inputs.cpu().numpy()\n",
    "train_labels = train_labels.cpu().numpy()\n",
    "train_dataset = FDTDDataset(train_inputs, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试数据标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [28:29<00:00,  8.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([200, 1000, 11])\n",
      "torch.Size([200, 1000, 10])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_labels = torch.tensor([], device=device)\n",
    "test_inputs = torch.tensor([], device=device)\n",
    "for data in tqdm(test_data):\n",
    "    inputs = torch.tensor(data)\n",
    "    outputs = simulator(inputs).detach()\n",
    "    test_labels = torch.cat((test_labels, outputs), dim=0)\n",
    "\n",
    "    inputs = data_transformer(inputs).unsqueeze(0)\n",
    "    test_inputs = torch.cat((test_inputs, inputs), dim=0)\n",
    "\n",
    "print(test_inputs.shape)\n",
    "print(test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_inputs = test_inputs.cpu().numpy()\n",
    "test_labels = test_labels.cpu().numpy()\n",
    "test_dataset = FDTDDataset(test_inputs, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_save_path = \"data/train_dataset.pth\"\n",
    "test_dataset_save_path = \"data/test_dataset.pth\"\n",
    "\n",
    "torch.save(train_dataset, train_dataset_save_path)\n",
    "torch.save(test_dataset, test_dataset_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_save_path = \"data/train_dataset.pth\"\n",
    "test_dataset_save_path = \"data/test_dataset.pth\"\n",
    "\n",
    "train_dataset = torch.load(train_dataset_save_path, weights_only=False)\n",
    "test_dataset = torch.load(test_dataset_save_path, weights_only=False)\n",
    "\n",
    "# 设置PyTorch默认数据类型为float32\n",
    "torch.set_default_dtype(torch.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from data/model.pth\n"
     ]
    }
   ],
   "source": [
    "model = LSTMPredictor(\n",
    "    input_size=10,\n",
    "    hidden_size=128,\n",
    "    output_size=10,\n",
    "    num_layers=4,\n",
    "    dropout=0.1\n",
    ").to(device)\n",
    "\n",
    "model_save_path = \"data/model.pth\"\n",
    "if os.path.exists(model_save_path):\n",
    "    model.load_state_dict(torch.load(model_save_path, weights_only=True))\n",
    "    print(\"model loaded from\", model_save_path)\n",
    "else:\n",
    "    print(\"model not found, training from scratch\")\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总参数量: 469,770\n",
      "\n",
      "模型结构与参数详情:\n",
      "lstm.weight_ih_l0    | [512, 11]                 | 5,632\n",
      "lstm.weight_hh_l0    | [512, 128]                | 65,536\n",
      "lstm.bias_ih_l0      | [512]                     | 512\n",
      "lstm.bias_hh_l0      | [512]                     | 512\n",
      "lstm.weight_ih_l1    | [512, 128]                | 65,536\n",
      "lstm.weight_hh_l1    | [512, 128]                | 65,536\n",
      "lstm.bias_ih_l1      | [512]                     | 512\n",
      "lstm.bias_hh_l1      | [512]                     | 512\n",
      "lstm.weight_ih_l2    | [512, 128]                | 65,536\n",
      "lstm.weight_hh_l2    | [512, 128]                | 65,536\n",
      "lstm.bias_ih_l2      | [512]                     | 512\n",
      "lstm.bias_hh_l2      | [512]                     | 512\n",
      "lstm.weight_ih_l3    | [512, 128]                | 65,536\n",
      "lstm.weight_hh_l3    | [512, 128]                | 65,536\n",
      "lstm.bias_ih_l3      | [512]                     | 512\n",
      "lstm.bias_hh_l3      | [512]                     | 512\n",
      "output_layer.weight  | [10, 128]                 | 1,280\n",
      "output_layer.bias    | [10]                      | 10\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "# 打印总参数量\n",
    "total_params = count_parameters(model)\n",
    "print(f'总参数量: {total_params:,}')\n",
    "\n",
    "# 打印每层参数的详细信息\n",
    "print('\\n模型结构与参数详情:')\n",
    "for name, param in model.named_parameters():\n",
    "    print(f'{name:20} | {str(list(param.size())):25} | {param.numel():,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 100000\n",
    "learning_rate = 1e-3\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型保存路径：`model_save_path = \"data/model.pth\"`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Progress:   0%|          | 0/1900000 [00:00<?, ?it/s]"
     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 722.00 MiB. GPU 0 has a total capacity of 3.71 GiB of which 667.38 MiB is free. Process 568131 has 2.22 GiB memory in use. Including non-PyTorch memory, this process has 830.00 MiB memory in use. Of the allocated memory 735.63 MiB is allocated by PyTorch, and 12.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 18\u001b[0m\n\u001b[1;32m     14\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m     16\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 18\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n\u001b[1;32m     20\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n",
      "File \u001b[0;32m~/miniconda3/envs/OpTorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/OpTorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/workspace/pyDistilledFDTD/simulation/student/models.py:66\u001b[0m, in \u001b[0;36mLSTMPredictor.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     63\u001b[0m inputs \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mrequires_grad_(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     65\u001b[0m \u001b[38;5;66;03m# LSTM处理\u001b[39;00m\n\u001b[0;32m---> 66\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     68\u001b[0m output_seq \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_layer(lstm_out)\n\u001b[1;32m     70\u001b[0m intensity_seq \u001b[38;5;241m=\u001b[39m output_seq\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m2\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/OpTorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/OpTorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
      "File \u001b[0;32m~/miniconda3/envs/OpTorch/lib/python3.11/site-packages/torch/nn/modules/rnn.py:1123\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m   1120\u001b[0m         hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[1;32m   1122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1123\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1124\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1130\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1133\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1134\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1135\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[1;32m   1136\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m   1137\u001b[0m         batch_sizes,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1144\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[1;32m   1145\u001b[0m     )\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 722.00 MiB. GPU 0 has a total capacity of 3.71 GiB of which 667.38 MiB is free. Process 568131 has 2.22 GiB memory in use. Including non-PyTorch memory, this process has 830.00 MiB memory in use. Of the allocated memory 735.63 MiB is allocated by PyTorch, and 12.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。\n",
      "\u001b[1;31m请查看单元格中的代码，以确定故障的可能原因。\n",
      "\u001b[1;31m单击<a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>了解详细信息。\n",
      "\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
     ]
    }
   ],
   "source": [
    "total_steps = num_epochs * len(train_loader)\n",
    "progress_bar = tqdm(total=total_steps, desc=\"Training Progress\")\n",
    "\n",
    "last_epoch_loss = \"\"\n",
    "min_epoch_loss = float('inf')\n",
    "best_epochs = 0\n",
    "\n",
    "with torch.enable_grad():\n",
    "    for epoch in range(num_epochs):\n",
    "        epoch_loss = 0\n",
    "        model.train()\n",
    "        for inputs, labels in train_loader:\n",
    "            inputs = inputs.to(device, dtype=torch.float32)\n",
    "            labels = labels.to(device, dtype=torch.float32)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            progress_bar.update(1)\n",
    "            progress_bar.set_description(f\"Batch Loss: {loss.item():.6f}   \" + last_epoch_loss + \"   \" + f\"min_epoch_loss: {min_epoch_loss}\")\n",
    "\n",
    "        last_epoch_loss = f\"Epoch {epoch+1}/{num_epochs} Loss: {epoch_loss/len(train_loader):.6f}\"\n",
    "        \n",
    "        if epoch_loss < min_epoch_loss:\n",
    "            min_epoch_loss = epoch_loss\n",
    "            torch.save(model.state_dict(), model_save_path)\n",
    "            best_epochs = epoch\n",
    "\n",
    "\n",
    "progress_bar.close() \n",
    "print(f\"Best Epochs: {best_epochs}; Best Loss: {min_epoch_loss:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTMPredictor(\n",
       "  (lstm): LSTM(11, 128, num_layers=4, batch_first=True, dropout=0.1)\n",
       "  (output_layer): Linear(in_features=128, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_save_path = \"data/model.pth\"\n",
    "model = LSTMPredictor(\n",
    "    input_size=10,\n",
    "    hidden_size=128,\n",
    "    output_size=10,\n",
    "    num_layers=4,\n",
    "    dropout=0.1\n",
    ").to(device)\n",
    "model.load_state_dict(torch.load(model_save_path, weights_only=True))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test Loss: 0.031227\n"
     ]
    }
   ],
   "source": [
    "test_loss = 0\n",
    "\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs = inputs.to(device, dtype=torch.float32)\n",
    "        labels = labels.to(device, dtype=torch.float32)\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        test_loss += loss.item()\n",
    "\n",
    "        all_preds.append(outputs.cpu())\n",
    "        all_labels.append(labels.cpu())\n",
    "\n",
    "# 计算平均测试损失\n",
    "avg_test_loss = test_loss / len(test_loader)\n",
    "print(f\"Average Test Loss: {avg_test_loss:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.000562\n",
      "Mean Absolute Error: 0.020395\n",
      "R² Score: 0.766181\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将预测和标签转换为numpy数组以便绘图\n",
    "all_preds = torch.cat(all_preds, dim=0).numpy()\n",
    "all_labels = torch.cat(all_labels, dim=0).numpy()\n",
    "\n",
    "# 绘制预测结果与真实值的对比\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 选择一个样本,并对时间维度取平均\n",
    "sample_idx = 100  # 可以改变这个索引来查看不同的样本\n",
    "preds_mean = np.mean(all_preds[sample_idx], axis=0)  # 对时间维度取平均\n",
    "labels_mean = np.mean(all_labels[sample_idx], axis=0)  # 对时间维度取平均\n",
    "\n",
    "# 绘制预测值和真实值的对比\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(preds_mean, label='Predicted')\n",
    "plt.plot(labels_mean, label='True')\n",
    "plt.title(f'Prediction vs True (Sample {sample_idx}, Time Averaged)')\n",
    "plt.xlabel('Port')\n",
    "plt.ylabel('Intensity')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制散点图\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(labels_mean, preds_mean)\n",
    "plt.plot([0, 1], [0, 1], 'r--')  # 理想的对角线\n",
    "plt.title('Prediction vs True (Scatter)')\n",
    "plt.xlabel('True Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 计算一些统计指标 (使用时间平均后的数据)\n",
    "mse = np.mean((preds_mean - labels_mean) ** 2)\n",
    "mae = np.mean(np.abs(preds_mean - labels_mean))\n",
    "r2 = 1 - np.sum((preds_mean - labels_mean) ** 2) / np.sum((labels_mean - np.mean(labels_mean)) ** 2)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse:.6f}\")\n",
    "print(f\"Mean Absolute Error: {mae:.6f}\")\n",
    "print(f\"R² Score: {r2:.6f}\")\n",
    "\n",
    "# 可视化时序预测\n",
    "plt.figure(figsize=(15, 5))\n",
    "time_steps = np.arange(all_preds.shape[1])\n",
    "port_idx = 0  # 可以改变这个索引来查看不同端口的预测\n",
    "\n",
    "plt.plot(time_steps, all_labels[sample_idx, :, port_idx], label='True')\n",
    "plt.plot(time_steps, all_preds[sample_idx, :, port_idx], label='Predicted')\n",
    "plt.title(f'Time Series Prediction (Sample {sample_idx}, Port {port_idx})')\n",
    "plt.xlabel('Time Step')\n",
    "plt.ylabel('Intensity')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "optorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
